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    Area of Science:

    • Machine Learning
    • Ensemble Methods
    • Statistical Sampling

    Background:

    • Bagging is a popular ensemble method using simple random sampling (SRS) with replacement.
    • Existing methods for imbalanced data alter distributions rather than simulating them effectively.
    • Ranked set sampling (RSS) offers a more effective sampling approach using auxiliary information.

    Purpose of the Study:

    • To introduce a novel bagging ensemble method based on ranked set sampling (RSS).
    • To leverage RSS's ability to create more effective training sets through ordered sampling.
    • To provide a theoretical explanation for the enhanced performance of RSS-Bagging.

    Main Methods:

    • Developed a bagging ensemble method incorporating ranked set sampling (RSS).
    • Derived a generalization bound for the ensemble from posterior probability estimation and Fisher information perspectives.
    • Conducted experiments on 12 benchmark datasets comparing RSS-Bagging with SRS-Bagging.

    Main Results:

    • RSS-Bagging demonstrated statistically superior performance compared to SRS-Bagging.
    • The theoretical bound highlights that RSS samples possess higher Fisher information than SRS samples.
    • The improvement was particularly evident when using multinomial logistic regression (MLR) and support vector machine (SVM) as base classifiers.

    Conclusions:

    • RSS-Bagging offers a statistically significant improvement over traditional SRS-Bagging.
    • The theoretical framework supports the enhanced performance of RSS-Bagging due to increased Fisher information.
    • This method provides a more effective approach to ensemble learning, especially for imbalanced datasets.